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README.md

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+---
+title: ControlNet Canny
+emoji: ⚡
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 5.28.0
+app_file: app.py
+pinned: false
+---
+
+# ControlNet · Canny Edge
+
+Lade ein Bild hoch, schreib einen Prompt – und erzeuge ein neues Bild,
+das die Kanten/Struktur deines Originals übernimmt.
+
+Läuft auf CPU (langsam, ~2–5 Min pro Bild). Für schnellere Ergebnisse
+in den Space-Settings auf GPU upgraden – der Code funktioniert ohne Änderung.

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+
+# ---------------------------------------------------------------------------
+# Device setup (works on free CPU Spaces)
+# ---------------------------------------------------------------------------
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
+
+# ---------------------------------------------------------------------------
+# Model loading (cached – runs once on Space startup)
+# ---------------------------------------------------------------------------
+def load_pipeline():
+    controlnet = ControlNetModel.from_pretrained(
+        "lllyasviel/sd-controlnet-canny",
+        torch_dtype=DTYPE,
+    )
+    pipe = StableDiffusionControlNetPipeline.from_pretrained(
+        "runwayml/stable-diffusion-v1-5",
+        controlnet=controlnet,
+        torch_dtype=DTYPE,
+        safety_checker=None,
+    )
+    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+    pipe = pipe.to(DEVICE)
+    if DEVICE == "cuda":
+        pipe.enable_model_cpu_offload()
+    return pipe
+
+pipe = load_pipeline()
+
+# ---------------------------------------------------------------------------
+# Helper: extract Canny edges
+# ---------------------------------------------------------------------------
+def extract_canny(image: Image.Image, low: int, high: int) -> Image.Image:
+    img_array = np.array(image.convert("RGB"))
+    edges = cv2.Canny(img_array, low, high)
+    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+    return Image.fromarray(edges_rgb)
+
+# ---------------------------------------------------------------------------
+# Main generation function
+# ---------------------------------------------------------------------------
+def generate(input_image, prompt, negative_prompt, canny_low, canny_high,
+             guidance_scale, steps, seed):
+    if input_image is None:
+        raise gr.Error("Bitte lade ein Bild hoch.")
+    if not prompt.strip():
+        raise gr.Error("Bitte gib einen Prompt ein.")
+
+    pil_image = Image.fromarray(input_image).resize((512, 512))
+    control_image = extract_canny(pil_image, int(canny_low), int(canny_high))
+
+    generator = torch.manual_seed(int(seed)) if seed >= 0 else None
+
+    result = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt or None,
+        image=control_image,
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance_scale),
+        generator=generator,
+    ).images[0]
+
+    return control_image, result
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+css = """
+body { font-family: 'Inter', sans-serif; background: #0f0f11; color: #e8e8f0; }
+.gradio-container { max-width: 1100px; margin: 0 auto; }
+#title { text-align: center; padding: 2rem 0 0.5rem; }
+#title h1 { font-size: 2rem; font-weight: 700; letter-spacing: -0.5px;
+             background: linear-gradient(90deg, #a78bfa, #60a5fa);
+             -webkit-background-clip: text; -webkit-text-fill-color: transparent; }
+#title p  { color: #9090a8; font-size: 0.95rem; margin-top: 0.25rem; }
+.panel    { background: #1a1a22; border: 1px solid #2a2a38; border-radius: 12px; padding: 1.25rem; }
+.generate-btn { background: linear-gradient(135deg, #7c3aed, #2563eb) !important;
+                color: white !important; border: none !important;
+                font-weight: 600 !important; font-size: 1rem !important;
+                border-radius: 8px !important; height: 48px !important; }
+.generate-btn:hover { opacity: 0.9 !important; }
+"""
+
+with gr.Blocks(css=css, title="ControlNet Canny") as demo:
+    gr.HTML("""
+        <div id="title">
+            <h1>⚡ ControlNet · Canny Edge</h1>
+            <p>Lade ein Bild hoch, schreib einen Prompt – und erzeuge ein neues Bild, das die Struktur deines Originals übernimmt.</p>
+        </div>
+    """)
+
+    gr.Markdown(f"> 🖥️ Läuft auf: **{DEVICE.upper()}** — auf CPU dauert eine Generierung ca. 2–5 Minuten. Bitte Geduld.")
+
+    with gr.Row():
+        with gr.Column(scale=1, elem_classes="panel"):
+            gr.Markdown("### 📥 Eingabe")
+            input_image = gr.Image(label="Referenzbild", type="numpy", height=300)
+            prompt = gr.Textbox(label="Prompt",
+                placeholder="a futuristic city at night, neon lights, photorealistic, 8k", lines=3)
+            negative_prompt = gr.Textbox(label="Negative Prompt (optional)",
+                placeholder="blurry, low quality, watermark, deformed", lines=2)
+
+            with gr.Accordion("⚙️ Erweiterte Einstellungen", open=False):
+                with gr.Row():
+                    canny_low  = gr.Slider(0, 255, value=100, step=1, label="Canny Low Threshold")
+                    canny_high = gr.Slider(0, 255, value=200, step=1, label="Canny High Threshold")
+                with gr.Row():
+                    guidance_scale = gr.Slider(1, 20, value=7.5, step=0.5, label="Guidance Scale")
+                    steps          = gr.Slider(10, 30, value=15, step=1, label="Inference Steps")
+                seed = gr.Number(value=42, label="Seed (-1 = zufällig)", precision=0)
+
+            run_btn = gr.Button("🎨  Generieren", elem_classes="generate-btn")
+
+        with gr.Column(scale=1, elem_classes="panel"):
+            gr.Markdown("### 📤 Ergebnis")
+            canny_out  = gr.Image(label="Canny-Kantenbild", height=250)
+            result_out = gr.Image(label="Generiertes Bild", height=350)
+
+    run_btn.click(
+        fn=generate,
+        inputs=[input_image, prompt, negative_prompt, canny_low, canny_high,
+                guidance_scale, steps, seed],
+        outputs=[canny_out, result_out],
+    )
+
+demo.queue().launch()

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+torchvision
+diffusers
+transformers
+accelerate
+opencv-python-headless
+Pillow
+numpy
+gradio